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Yi Wang - One of the best experts on this subject based on the ideXlab platform.
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a probabilistic extension of action language bc
arXiv: Artificial Intelligence, 2018Co-Authors: Joohyung Lee, Yi WangAbstract:We present a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LPMLN programs---a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, Postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LPMLN.
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A Probabilistic Extension of Action Language BC+
2018Co-Authors: Lee Joohyung, Yi WangAbstract:We present a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LPMLN programs---a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, Postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LPMLN.Comment: Paper presented at the 34nd International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages, LaTeX, 1 PDF figures (arXiv:YYMM.NNNNN
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Probabilistic Action Language pBC+
OASIcs - OpenAccess Series in Informatics. Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018), 2018Co-Authors: Yi WangAbstract:We present an ongoing research on a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LP^{MLN} programs - a probabilistic extension of answer set programs. As preliminary results accomplished, we illustrate how probabilistic reasoning about transition systems, such as prediction, Postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LP^{MLN}. For future work, we plan to develop a compiler that automatically translates pBC+ description into LP^{MLN} programs, as well as parameter learning in probabilistic action domains through LP^{MLN} weight learning. We will work on defining useful extensions of pBC+ to facilitate hypothetical/counterfactual reasoning. We will also find real-world applications, possibly in robotic domains, to empirically study the performance of this approach to probabilistic reasoning in action domains
Eppe Manfred - One of the best experts on this subject based on the ideXlab platform.
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Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction
2019Co-Authors: Eppe ManfredAbstract:We present an epistemic action theory for tractable epistemic reasoning as an extension to the h-approximation (HPX) theory. In contrast to existing tractable approaches, the theory supports functional fluents and postdictive reasoning with static causal laws. We argue that this combination is particularly synergistic because it allows one not only to perform direct Postdiction about the conditions of actions, but also indirect Postdiction about the conditions of static causal laws. We show that despite the richer expressiveness, the temporal projection problem remains tractable (polynomial), and therefore the planning problem remains in NP. We present the operational semantics of our theory as well as its formulation as Answer Set Programming.Comment: There are flaws in the mathematical backgroun
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Postdiktives Schließen in Epistemischer Aktionstheorie
FB3 Mathematik Informatik, 2013Co-Authors: Eppe ManfredAbstract:If an agent executes an action, this will not only change the world physically, but also the agent's knowledge about the world. Therefore the occurrence of an action can be modeled as an epistemic state transition which maps the knowledge state of an agent to a successor knowledge state. For example, consider that an agent in a state s_0 executes an action a. This causes a transition to a state s_1. Subsequently, the agent executes a sensing action a_s, which produces knowledge and causes a transition to a state s_2. With the information which is gained by the sensation, the agent can not only extend its knowledge about s_2, but also infer additional knowledge about the initial state s_0. That is, the agent uses knowledge about the present to retrospectively acquire additional information about the past. We refer to this temporal form of epistemic inference as Postdiction. Existing action theories are not capable of efficiently performing postdictive reasoning because they require an exponential number of state variables to represent an agent's knowledge state. The contribution of this thesis is an approximate epistemic action theory which is capable of postdictive reasoning while it requires only a linear number of state variables to represent an agent's knowledge state. In addition, the theory is able to perform a more general temporal form of Postdiction, which most existing approaches do not support. We call the theory the h-approximation (HPX) because it explicitly represents "historical" knowledge about past world states. In addition to the operational semantics of HPX, we present its formalization in terms of Answer Set Programming (ASP) and provide respective soundness results. The ASP implementation allows us to apply HPX in real robotic applications by using off-the-shelf ASP solvers. Specifically, we integrate of HPX in an online planning framework for Cognitive Robotics where planning, plan execution and abductive explanation tasks are interleaved. As a proof-of-concept, we provide a case-study which demonstrates the application of HPX for high-level robot control in a Smart Home. The case-study emphasizes the usefulness of Postdiction for abnormality detection in robotics: actions which are performed by robots are often not successful due to unforeseen practical problems. A solution is to verify action success by observing the effects of the action. If the desired effects do not hold after action execution, then one can postdict the existence of an abnormality
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Postdictive Reasoning in Epistemic Action Theory
2013Co-Authors: Eppe ManfredAbstract:If an agent executes an action, this will not only change the world physically, but also the agent's knowledge about the world. Therefore the occurrence of an action can be modeled as an epistemic state transition which maps the knowledge state of an agent to a successor knowledge state. For example, consider that an agent in a state s_0 executes an action a. This causes a transition to a state s_1. Subsequently, the agent executes a sensing action a_s, which produces knowledge and causes a transition to a state s_2. With the information which is gained by the sensation, the agent can not only extend its knowledge about s_2, but also infer additional knowledge about the initial state s_0. That is, the agent uses knowledge about the present to retrospectively acquire additional information about the past. We refer to this temporal form of epistemic inference as Postdiction. Existing action theories are not capable of efficiently performing postdictive reasoning because they require an exponential number of state variables to represent an agent's knowledge state. The contribution of this thesis is an approximate epistemic action theory which is capable of postdictive reasoning while it requires only a linear number of state variables to represent an agent's knowledge state. In addition, the theory is able to perform a more general temporal form of Postdiction, which most existing approaches do not support. We call the theory the h-approximation (HPX) because it explicitly represents historical knowledge about past world states. In addition to the operational semantics of HPX, we present its formalization in terms of Answer Set Programming (ASP) and provide respective soundness results. The ASP implementation allows us to apply HPX in real robotic applications by using off-the-shelf ASP solvers. Specifically, we integrate of HPX in an online planning framework for Cognitive Robotics where planning, plan execution and abductive explanation tasks are interleaved. As a proof-of-concept, we provide a case-study which demonstrates the application of HPX for high-level robot control in a Smart Home. The case-study emphasizes the usefulness of Postdiction for abnormality detection in robotics: actions which are performed by robots are often not successful due to unforeseen practical problems. A solution is to verify action success by observing the effects of the action. If the desired effects do not hold after action execution, then one can postdict the existence of an abnormality
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h-approximation: History-Based Approximation of Possible World Semantics as ASP
2013Co-Authors: Eppe Manfred, Bhatt Mehul, Dylla FrankAbstract:We propose an approximation of the Possible Worlds Semantics (PWS) for action planning. A corresponding planning system is implemented by a transformation of the action specification to an Answer-Set Program. A novelty is support for Postdiction wrt. (a) the plan existence problem in our framework can be solved in NP, as compared to $\Sigma_2^P$ for non-approximated PWS of Baral(2000); and (b) the planner generates optimal plans wrt. a minimal number of actions in $\Delta_2^P$. We demo the planning system with standard problems, and illustrate its integration in a larger software framework for robot control in a smart home.Comment: 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2013
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Narrative based Postdictive Reasoning for Cognitive Robotics
University of Bremen Bremen Germany, 2013Co-Authors: Eppe Manfred, Bhatt MehulAbstract:Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems. An added challenge, even when suitably specialised action languages and reasoning systems exist, is practical integration and application within large-scale robot control frameworks. In the backdrop of an autonomous wheelchair robot control task, we report on application-driven work to realise Postdiction triggered abnormality detection and re-planning for real-time robot control: (a) Narrative-based knowledge about the environment is obtained via a larger smart environment framework; and (b) abnormalities are postdicted from stable-models of an answer-set program corresponding to the robot's epistemic model. The overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer-set programming.arXiv:1306.0665
Maneesh Sahani - One of the best experts on this subject based on the ideXlab platform.
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a neurally plausible model for online recognition and Postdiction in a dynamical environment
bioRxiv, 2020Co-Authors: Li Kevin Wenliang, Maneesh SahaniAbstract:Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts---which are informed both by sensory evidence and by prior expectations about the structure of the environment. It is suggested that the brain does so using the statistical structure provided by an internal model of how latent, causal factors produce the observed patterns. In dynamic environments, such integration often takes the form of Postdiction, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learnt efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.
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a neurally plausible model for online recognition and Postdiction in a dynamical environment
Neural Information Processing Systems, 2019Co-Authors: Li Kevin Wenliang, Maneesh SahaniAbstract:Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts---which are informed both by sensory evidence and by prior expectations about the structure of the environment. It is suggested that the brain does so using the statistical structure provided by an internal model of how latent, causal factors produce the observed patterns. In dynamic environments, such integration often takes the form of \emph{Postdiction}, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learnt efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.
Shinsuke Shimojo - One of the best experts on this subject based on the ideXlab platform.
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What you saw is what you will hear: Two new illusions with audiovisual postdictive effects
2018Co-Authors: Noelle R. B. Stiles, Carmel A. Levitan, Yukiyasu Kamitani, Shinsuke ShimojoAbstract:Neuroscience investigations are most often focused on the prediction of future perception or decisions based on prior brain states or stimulus presentations. However, the brain can also process information retroactively, such that later stimuli impact conscious percepts of the stimuli that have already occurred (called “Postdiction”). Postdictive effects have thus far been mostly unimodal (such as apparent motion), and the models for Postdiction have accordingly been limited to early sensory regions of one modality. We have discovered two related multimodal illusions in which audition instigates postdictive changes in visual perception. In the first illusion (called the “Illusory Audiovisual Rabbit”), the location of an illusory flash is influenced by an auditory beep-flash pair that follows the perceived illusory flash. In the second illusion (called the “Invisible Audiovisual Rabbit”), a beep-flash pair following a real flash suppresses the perception of the earlier flash. Thus, we showed experimentally that these two effects are influenced significantly by Postdiction. The audiovisual rabbit illusions indicate that Postdiction can bridge the senses, uncovering a relatively-neglected yet critical type of neural processing underlying perceptual awareness. Furthermore, these two new illusions broaden the Double Flash Illusion, in which a single real flash is doubled by two sounds. Whereas the double flash indicated that audition can create an illusory flash, these rabbit illusions expand audition’s influence on vision to the suppression of a real flash and the relocation of an illusory flash. These new additions to auditory-visual interactions indicate a spatio-temporally fine-tuned coupling of the senses to generate perception.
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Postdiction its implications on visual awareness hindsight and sense of agency
Frontiers in Psychology, 2014Co-Authors: Shinsuke ShimojoAbstract:There are a few postdictive perceptual phenomena known, in which a stimulus presented later seems causally to affect the percept of another stimulus presented earlier. While backward masking provides a classical example, the flash lag effect stimulates theorists with a variety of intriguing findings. The TMS-triggered scotoma together with “backward filling-in” of it offer a unique neuroscientific case. Findings suggest that various visual attributes are reorganized in a postdictive fashion to be consistent with each other, or to be consistent in a causality framework. In terms of the underlying mechanisms, four prototypical models have been considered: the “catch up,” the “reentry,” the “different pathway” and the “memory revision” models. By extending the list of postdictive phenomena to memory, sensory-motor and higher-level cognition, one may note that such a postdictive reconstruction may be a general principle of neural computation, ranging from milliseconds to months in a time scale, from local neuronal interactions to long-range connectivity, in the complex brain. The operational definition of the “postdictive phenomenon” can be applicable to such a wide range of sensory/cognitive effects across a wide range of time scale, even though the underlying neural mechanisms may vary across them. This has significant implications in interpreting “free will” and “sense of agency” in functional, psychophysical and neuroscientific terms.
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Biology and Biological
2014Co-Authors: Shinsuke Shimojo, Yuki Yamada, Takahiro KawabeAbstract:Postdiction: its implications on visual awareness
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stopping the motion and sleuthing the flash lag effect spatial uncertainty is the key to perceptual mislocalization
Vision Research, 2004Co-Authors: Ryota Kanai, Bhavin R Sheth, Shinsuke ShimojoAbstract:A moving object is perceived to lie beyond a static object presented at the same time at the same retinal location (flash-lag effect or FLE). Some studies report that if the moving stimulus stops moving (flash-terminated condition or FTC) the instant the flash occurs, a FLE does not occur. Other studies, using different stimuli, report that the FLE does, in fact, occur in the FTC. The FTC is thus a crucial turning point in theories of flash-lag. Unraveling the mystery of the FLE in the FTC will help unravel the mechanisms underpinning flash-lag and perhaps even perceptual localization in general. Our experiments show that eccentricity of the moving stimulus was a contributing factor, as were eccentricity of the flashed stimulus and spatial separation between the two stimuli. Other factors, such as contrast and offset of moving stimulus, also modulate the magnitude of the FLE in the FTC. We surmise that uncertainty in determining the position in space of a moving stimulus is a key requirement for the lag-effect. A lag-effect in the FTC challenges influential models, such as differential latency, motion extrapolation, and Postdiction. Based partly on the notion of an asymmetric spread of activity that arises because of the sheer nature of motion and from a combination of established physiological mechanisms, we propose a schematic account of the present findings that subsumes previous psychological models and scaffolds past experimental findings.
Joohyung Lee - One of the best experts on this subject based on the ideXlab platform.
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a probabilistic extension of action language bc
arXiv: Artificial Intelligence, 2018Co-Authors: Joohyung Lee, Yi WangAbstract:We present a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LPMLN programs---a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, Postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LPMLN.